Scale-up Systems India Mettler RC1 Sanket Salgaonkar

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Scale-up Systems

Scale-up Scale-up calculations calculations

using RC1e data using RC1e data in DynoChemin DynoChem

Sanket Salgaonkar

Scale-up Systems India


Scale-up Systems

IntroductionIntroduction

3©2012 Scale-up Systems Ltd. All Rights Reserved. www.scale-up.com

DynoChem in Pharma IndustryDynoChem in Pharma Industry

Leading software for chemical process understanding

Scientists and engineers Development, scale-up, tech transfer, troubleshooting and

continuous improvement Unit ops with multiple phases, reactions, heat and mass

transfer Batch, fed batch or continuous operating modes

Answers “what happens if …?” and “how to achieve …?”

Facilitates R&D productivity, collaboration, material supply, QbD efforts


Model based approach to Process Development

Lab Experimen

ts

Data (Model)

Process Understanding based Model Generation

Model + Equipment data Large Scale

Process Optimization

Design

Predicted Performanc

e

Scale-up

Experiments are performed to generate Process Understanding, not necessarily to get good yields in the lab

This Process Understanding is then captured by First Principles Mechanistic Models


A + B C k1(Tref) Ea1 dHr1

C P k2(Tref) Ea2 dHr2

Basis for Model development are time resolved profiles of different data from experiments at different conditions (temperatures, starting concentrations)

1) Analytical profiles

2) Heat generation rates

3) Additional online info (ReactIR, pH, gas generation, H2 uptake, etc...)

4) Accurate temperature profiles


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Modeling Modeling ΔΔHHR R in in DynoChemDynoChem


Sources of Sources of ΔΔHHR R data data

The standard sources of ΔHR are experimental data from

Reaction Calorimeters for desired reaction conditions DSC and Adiabatic Calorimeters for exploring Process Safety (potential

runaway reactions)

The Mettler RC1 benchmark Reaction Calorimeter generates heat flow profiles, which can be entered into DynoChem

Reaction Rate Exp. kref Ea

A + B --> P k [A] [B] k> 0.0027 L/mol.sEa> 59.997 kJ/mol

A + P --> SP k [A] [P] k> 5.025E-4 L/mol.s Ea> 90.011 kJ/mol


Using RC1 DataUsing RC1 Data

Bulk liquid.Temperature (Imp) (C)A feed.Qv (Imp) (ml/min)Bulk liquid.Qr (Exp) (J/s)Bulk liquid.Qr (J/s)

RC1 run

Time (min)

Proc

ess

prof

ile (s

ee le

gend

)

0.0 60.0 120.0 180.0 240.0 300.00.0

14.0

28.0

42.0

56.0

70.0

Qr = r ΔHr V, where r is the reaction rate of this reaction.

Qr = (Σri ΔHri ) V, since a calorimeter measures the sum of all the heat flows.

Reaction Rate Exp. kref Tref Ea

A + B --> P k [A] [B] k> 0.0027 L/mol.s 60.0 C Ea> 59.997kJ/mol

A + P --> SP k [A] [P] k> 5.025E-4 L/mol.s 60.0 C Ea>90.011 kJ/mol


Elucidation of ΔHElucidation of ΔHR R from Qfrom Qr r DataData

Reaction heats are adjusted to experimental Qr data by fitting in DynoChem.

Reaction dHr

A + B --> P -149.86 kJ/mol

A + P --> SP -70.0 kJ/mol


Predictions from the Predictions from the process modelprocess model

Bulk liquid.SP (mol)Bulk liquid.P (mol)Bulk liquid.B (mol)Bulk liquid.A (mol)Bulk liquid.Volume (L)Feed vessel.Volume (L)

RC1 run

Time (min)

Proc

ess

prof

ile (

see

lege

nd)

0.0 60.0 120.0 180.0 240.0 300.00.0

0.3

0.6

0.9

1.2

1.5


Using ARC / DSC DataUsing ARC / DSC Data


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The Impact of Process The Impact of Process Safety – What-if Safety – What-if

Scenarios Scenarios


Process Safety ConcernsProcess Safety Concerns

Reaction energy coupled with the reaction rates is responsible for heat generation rates and that these with the interplay of reactor cooling capacity will determine the reaction temperature profile.

Typical What-if Scenarios

Loss of Cooling Capacity Consequences of a cooling failure

Reaction time at which loss of cooling capacity is critical

Time for counter measures


Process Safety ConcernsProcess Safety ConcernsTMR =f(MTSR) ;TMR: Time for Maximum Rate

MTSR: Maximum Temperature of a Synthesis Reaction

R. Gigax Chem.Eng.Sci., 1988, 43, 1759)


Why DynoChem?Why DynoChem?

DynoChem predicts :

Temperature profiles and their consequences before any real large scale reaction is performed.

The fate of reaction energy under deviations from the desired conditions

The behaviour of TMR vs Temperature by allowing the automated run of multiple scenarios (like running DoE type experiments)


TMR v/s TrTMR v/s Tr


Effect of Temp & Dosing Effect of Temp & Dosing onTMR onTMR


Easy Scale-up of RC1 Easy Scale-up of RC1


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Case StudiesCase Studies


AbbottAbbott


Dynochem modeling of an unstable cryogenic reaction decomposition of unstable Aryllithium solutions with two exotherms – Heat of Addition (feed-limited) and Heat of decomposition (T-dependent)

MerckMerck


Janssen Pharmaceutica, Janssen Pharmaceutica, BelgiumBelgium


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ConclusionConclusion


ConclusionsConclusionsWhere DynoChem Fits?

Provides a generic simulation engine that designed for solving rate based process models across many unit operations and model complexities

Comes with a substantial model library “out of the box” that include many developed with your colleagues in the safety community

Is inherently extendable allowing templates to be customized to your specific workgroup and workflow.

The Role of Safety

The potential for safety to expand their role in pharmaceutical organizations has never been greater with QbD fueling a desire for predictive process knowledge

This starts just by using modeling tools to further leverage what you already do!


www.scale-up.comwww.scale-up.com


DynoChem ResourcesDynoChem Resources


Scale-up Expertise Scale-up Expertise

Principal Consultant – Dr Wilfried Hoffmann

Wilfried has over 28 years experience working in the pharmaceutical industry, most recently with Pfizer in the UK and Germany. Wilfried has a PhD in Organic Chemistry from Ruhr-Universität Bochum and has a special interest in Thermochemical Kinetics.

[email protected]


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Thanks and Thanks and Wrap-upWrap-up

Scale-up Systems India Mettler RC1 Sanket Salgaonkar

Technology

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